[rgb]0,0,0The encoder-agnostic methods [rgb]0,0,0[1]}, [2]}, [3]} utilize task-agnostic features of videos extracted from a vanilla feature encoder denoted as \(E\)  (C3D [4]} or I3D [5]}) to estimate anomaly scores.
The encoder-based methods [rgb]0,0,0[6]}, [7]} train both the feature encoder and classifier simultaneously.
[rgb]0,0.,0The state-of-the-art encoder-based method is
Zhong et al. [7]}, which formulates WS-VAD as a label noise learning problem
[rgb]0,0.,0and learns from the noisy labels filtered by a label noise cleaner network.
However, label noise results from assigning video-level labels to each clip. Even though the cleaner network corrects some of the noisy labels [rgb]0,0,0in the time-consuming iterative optimization,
[rgb]0,0,0the refinement of representations progresses slowly 
as [rgb]0,0, 0these models are [rgb]0.,0.,0mistaught by [rgb]0,0,0seriously noisy pseudo labels
at the beginning.
